WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
Summary: arXiv:2604.08958v1 Announce Type: cross
Introduction
Reinforcement learning (RL) has emerged as a powerful paradigm for training agents in various environments, particularly in robotics. However, the practical application of RL is often hampered by the high costs and risks associated with data collection. This limitation has spurred interest in the concept of experience transfer, which involves leveraging data from a source task to enhance learning in a target task.
Challenges in Offline-to-Online Reinforcement Learning
The conventional approach of offline-to-online RL typically relies on a pre-defined dataset, often neglecting the dynamics of generating reliable data for effective transfer. This presents a significant challenge, as the quality and relevance of the data can directly influence the performance of the RL agent in the target task. The need for a more dynamic and efficient method for experience transfer is thus evident.
Introducing WOMBET
In response to these challenges, we introduce World Model-based Experience Transfer (WOMBET), a novel framework designed to enhance the transfer of experiences from a source task to a target task. WOMBET operates by learning a world model from the source task, which serves as a foundation for generating offline data through uncertainty-penalized planning.
Key Components of WOMBET
- World Model Learning: WOMBET begins by constructing a world model based on the source task. This model encapsulates the dynamics of the environment, allowing for realistic simulations of potential experiences.
- Data Generation: Utilizing the learned world model, WOMBET generates offline data through a method known as uncertainty-penalized planning. This approach ensures that the generated data is both relevant and informative.
- Trajectory Filtering: Following data generation, WOMBET employs a filtering mechanism that prioritizes trajectories yielding high returns while minimizing epistemic uncertainty. This filtering step is crucial for maintaining the quality of data used in the transfer process.
- Online Fine-Tuning: Finally, WOMBET facilitates online fine-tuning of the RL agent in the target task. This is achieved through adaptive sampling, which effectively balances the utilization of both offline and online data, enabling a smooth transition from prior-driven initialization to task-specific adaptation.
Empirical Results
Our empirical evaluations demonstrate that WOMBET significantly enhances sample efficiency and final performance when compared to established baselines on continuous control benchmarks. The framework’s ability to jointly optimize data generation and transfer showcases its potential to address the limitations of traditional RL approaches.
Conclusion
WOMBET represents a significant advancement in the field of reinforcement learning, particularly in its application to robotics. By effectively bridging the gap between offline and online learning, WOMBET not only improves the efficiency of experience transfer but also paves the way for more robust learning in complex environments. As the landscape of reinforcement learning continues to evolve, frameworks like WOMBET will be instrumental in unlocking the full potential of RL in real-world applications.